System and method for interaction routing by applying predictive analytics and machine learning to web and mobile application context

ABSTRACT

A system and method that pre-configure a telecommunication path between two users across a network. A server communicates an Application Programming Interface (API) to a website of an entity which, when accessed by a first user at a remote terminal, loads the API into the user terminal, captures data representative of the interaction of the first user with the website in real time, and communicates the data across the network to the server. The server compares the data with stored attributes of second users to identify a matching second user. The data is analyzed and used to predict a successful outcome between the two users. The server selects a network address of the matched second user, and, upon initiation of a telecommunication with the entity by the first user, routes the telecommunication to the network address and communicates the data to a second terminal of the matched second user.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser.14/686,404, titled “System and Method for Interaction Routing byApplying Predictive Analytics and Machine Learning to Web and MobileApplication Context”, filed on 14 Apr. 2015, which also claims priorityto and claims the benefit of U.S. Provisional Patent Application61/979,479, filed on 14 Apr. 2014, the specifications of which arehereby incorporated herein by reference

BACKGROUND OF THE INVENTION Field of the Invention

The application relates to a system and method for the interaction ofWeb Sites, Mobile Apps, Analytics and Contact Centers (Call Centers)through the use of technologies such as Predictive Analytics, MachineLearning, WebRTC and VoIP plugins for browsers.

Description of the Related Art

Nowadays, customers typically begin and conduct their interaction with agoods or service provider via the provider's website or mobileapplication, which are fast becoming the primary interfaces fororganizations to communicate with their customers, suppliers and otherstakeholders.

Traditional Contact Center routing to Customer Care Agents is done viadata derived from telephony parameters—e.g. Calling Line ID or viasimple choices made by customers via DTMF tones in Interactive VoiceResponse Sessions—e.g. Press 1 for Sales, 2 for Support, etc.

There is now a completely new paradigm possible for Call Centers wherebythe Customer begins the business interaction via the Business Web Siteor Mobile App, which are now the primary interfaces for businesses tosupport their customers. With the emergence of technologies like VoIPand WebRTC it is possible to build the Voice and Video functionalitydirectly into the Web Site and Browser or Mobile App based experience.This mechanism no longer requires a customer to even have a phone numberas the Media Path is established peer-to-peer between the Caller and theCallee's Browser or Mobile App.

The growing adoption of WebRTC, which is a proposed HTML5 standard, andfurther Internet-based telecommunications developments by the likes ofGoogle®, Cisco and others, for instance telephony plugins for webbrowsers such as GoogleTalk® or Cisco's Jabber Guest have made itpossible to build Unified Communications functions directly intobrowsers, to be invoked ad hoc by relevant embedded features of entitywebsites.

However, known implementations still only use such UnifiedCommunications plugins to initiate calls via telephony protocols, suchas SIP, into the PSTN, or an Enterprise PBX and then route the callsusing traditional methods like Calling Line ID or Dialed Number,together with either interactive voice response-based authentication(e.g. “enter your identifier”) or basic call routing (e.g. “press 1 forsupport, press 2 for sales”). Accordingly, such implementations stillonly use browsers as a gateway into the known and traditionalPSTN/PBX-based telecommunication model.

Interlocutors receiving such calls must thus rely upon either anydetails about the calling user which their data systems may alreadyhold, particularly if such a call is the first-ever real-timeinteraction of a customer with the entity associated with the website,or even both.

It is known to refine captured data with analytical data processingtools which track customer behavior and usage of online resources, likefor instance Google Analytics® which analyses website browsing patterns,however the output of such tools is usually aggregated and anonymizeddata, so are not suitable to support an intuitive and personalizedexperience for both of a calling user and their eventual interlocutor.Other tools (e.g. Marketo or Hub Spot) will rank visitors based onactivities on the Website. However these tools do not apply in extendingthis personalization to a Contact Centre mainly due to loss of contextwhen a customer switches communication to a telephone or other audiocommunication means. Other patent publications in the art includeUS2004/039775 and WO2014/071391, however again the systems and methodsdisclosed do not provide a satisfactory level of personalization forusers interacting with a website.

An improved method of routing calls of website users to relevantinterlocutors acting for the entities associated with the websites istherefore required, and a system embodying this method, which mitigateat least the above shortcomings of the prior art.

BRIEF SUMMARY OF THE INVENTION

The inventors have realized that emergent Wide Area Networktelecommunication technologies such as Voice-over-IP (“VoIP”) and WebRTCand analytic online data processing techniques can provide a synergisticeffect when combined.

The invention, as set out in the appended claims, provides techniquesfor routing remote interactions of users of web sites, mobileapplications and comparable network-distributed resources to relevantinterlocutors of entities associated with these network-distributedresource, based on the collection and analysis of data representativeof, for instance, user search terminology, webpage tags and meta-data,historical or sequential user page and link selections, user socialmedia data and the like. The techniques of the invention can usefully beaugmented with conducting such data analytics functions substantially inreal-time, both for pre-configuring a telecommunication of a user withone or more remote interlocutors on the fly, as the user keepsinteracting with one or more network-distributed resources and at leastuntil the user initiates such a telecommunication.

Accordingly, in a first aspect of the invention, a method ofpre-configuring a telecommunication path between at least two usersacross a network is provided, which comprises the steps of communicatingat least a first Application Programming Interface (API) to a website ofan entity; and, when the website is accessed at a first terminaloperated by a first of the said at least two users, loading the firstAPI at the first terminal; capturing data representative of theinteraction of the first user with the website at the first terminalwith the first API in real time; communicating the captured data acrossthe network to remote data processing means; comparing the communicatedcaptured data with attributes of a plurality of second users stored atthe remote data processing means for identifying a matching second userwherein the captured data is analyzed based on a pattern and used topredict a successful outcome between the first user and the matchedsecond user; and when the first user initiates a telecommunication withthe entity, selecting a network address of the matched second user atthe remote data processing means; routing the telecommunication to theselected network address; and communicating at least a portion of thecommunicated captured data to a second terminal operated by the selectedsecond user.

In an embodiment the captured data comprises personae and activity userdata and compared with stored attributes to identify the matching seconduser.

In an embodiment the pattern used to predict a successful outcome isbased on a machine learning step comprising a clustering technique tocluster specific patterns of captured data and store over time forcomparison with new captured data.

An embodiment of the method may advantageously comprise the further stepof inputting social media or other authentication data of the first userwhen the website is accessed. Other authentication data includes, butnot limited to, passwords or any private authentication service based onOAUTH2, AAA or similar.

In an embodiment of the method, the step of communicating the captureddata across the network to the remote data processing means may beperformed in real time.

In an embodiment of the method, the step of routing further may compriseselecting a telecommunication protocol and a network path between thefirst and second users.

In a variant of this embodiment, the telecommunication protocol may beselected for character-based messaging, audio-only or audio-videotelecommunication. In a further variant of this embodiment, the networkpath may be selected from at least one of VoIP, WebRTC, Jabber Guest;PSTN or other network path.

For any of these variants, the method step of selecting preferablyfurther comprises selecting the protocol and/or path as a function ofthe telecommunication functionalities of the first terminal and thecapabilities of the first user's network connection.

In a further aspect of the invention, a system is also provided forpre-configuring a telecommunication path between at least two usersacross a network, comprising a server for communicating at least a firstApplication Programming Interface (API) to a website of an entity; anetwork-connected terminal operated by a first of the said at least twousers and configured, upon access to the website, to load the first API,capture data representative of the interaction of the first user withthe website in real time, and communicate the captured data across thenetwork to the server; and a second network-connected terminal operatedby the second of the said at least two users; wherein the server isfurther configured to compare the communicated captured data with storedattributes of a plurality of second users for identifying a matchingsecond user wherein the captured data is analyzed based on a pattern andused to predict a successful outcome between the first user and thematched second user, select a network address of the matched second userand, upon initiation of a telecommunication with the entity by the firstuser, route the telecommunication to the selected network address, andcommunicate at least a portion of the communicated captured data to thesecond terminal.

In an embodiment of the system, the first terminal may be furtherconfigured to require inputting of social media or other authenticationdata by the first user when the website is accessed.

In an embodiment of the system, the first terminal may be furtherconfigured to communicate the captured data across the network to theserver in real time.

In an embodiment of the system, the server may be further configured toselect a telecommunication protocol and a network path between the firstand second users. The telecommunication protocol may again be selectedfor character-based messaging, audio-only or audio-videotelecommunication. The network path may again be selected from VoIP,WebRTC and PSTN.

In an embodiment of the system, the server may be further configured toselect as a function of the telecommunication functionalities of thefirst terminal and the capabilities of the first user's networkconnection.

According to yet another aspect of the present invention, there is alsoprovided a set of instructions recorded on a data carrying medium which,when processed by a first data processing terminal of an entityconnected to a network, configures the terminal to perform the steps ofcommunicating at least a first Application Programming Interface (API)to a website of an entity; causing the API to, when the website isaccessed at a second data processing terminal: load at the secondterminal; capture data representative of the interaction of a first userwith the website at the second terminal in real time; communicate thecaptured data across the network to the first data processing terminal;compare the communicated captured data with attributes of a plurality ofsecond users stored at the second data processing means for identifyinga matching second user wherein the captured data is analyzed based on apattern and used to predict a successful outcome between the first userand the matched second user; and when the first user initiates atelecommunication with the entity, of selecting a network address of thematched second user at the first terminal; routing the telecommunicationto the selected network address; and communicating at least a portion ofthe communicated captured data to a terminal operated by the selectedsecond user.

The set of instructions may be advantageously embodied as an applicationpackage file (‘APK’) for use with the Android™ operating system orembodied as an iPhone™ application archive (‘IPA’) for use with the iOS™operating system.

For any of the above embodiments and further variants, datarepresentative of the interaction preferably comprises at least oneselected from website data, website metadata, webpage page tags, websitecookies, website page headers, one or more user input or ‘click’streams, one or more search strings, a website navigation recordrepresentative of historical or sequential user page and linkselections, and user social media data.

For any of the above embodiments and further variants, the storedattributes of second users are preferably selected from alphanumeric,semantic, contextual and/or social media data uniquely associated witheach of the plurality of second users.

For any of the above embodiments and further variants the captured datacomprises personae and activity user data and compared with storedattributes to identify the matching second user.

In an embodiment there is provided a machine learning step or modulewherein the captured data is analyzed based on a pattern and used topredict a successful outcome between a first user and selected seconduser.

In an embodiment there is provided a machine learning step or modulecomprises a clustering technique to cluster specific patterns ofcaptured data and store over time for comparison with new captured data.

For any of the above embodiments and further variants, the website maybe a mobile application site or portal associated with the entitycapable of communicating with an application, or ‘app’, downloaded ontoa terminal.

For any of the above embodiments and further variants, any one of theplurality of data communication terminals and server may selected fromthe group comprising desktop computers, mobile telephone handsets,tablet computers, portable computers, personal digital assistants,portable media players, portable game consoles.

Other aspects are as set out in the claims herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects, features and advantages of at least oneembodiment of the invention will be more apparent from the followingmore particular description thereof, presented in conjunction with thefollowing drawings, wherein:

FIG. 1 shows a network environment comprising a communication networkand a plurality of data processing devices, consisting of both mobileand static terminals.

FIG. 2 is a logical diagram of a typical hardware architecture of amobile data processing and communication terminal shown in FIG. 1,including memory means.

FIG. 3 is a logical diagram of a typical hardware architecture of astatic data processing terminal shown in FIG. 1, including memory means.

FIG. 4 details data processing steps of an embodiment of the method ofthe invention, wherein the plurality of data processing devices of FIGS.1 to 3 communicate data and sets of instructions therebetween.

FIG. 5 is a block diagram illustration of the method of FIG. 4implemented in the system of FIGS. 1 to 3.

FIG. 6 is a logical diagram of the contents of the memory means of theuser mobile terminal shown in FIGS. 1 and 2, when performing the methodof FIG. 4 as a user terminal, including a website, captured interactiondata and a first set of instructions.

FIG. 7 is a logical diagram of the contents of the memory means of afirst data processing terminal shown in FIGS. 1 and 3, when performingthe method of FIG. 4 as a server, including the first set ofinstructions and captured interaction data of FIG. 6, a second set ofinstructions, analyzed interaction data and a database.

FIG. 8 is a logical diagram of the contents of the memory means ofanother data processing terminal shown in FIGS. 1 and 3, when performingthe method of FIG. 4 as an interlocutor terminal, including a browserand the analyzed data of FIG. 7.

DETAILED DESCRIPTION OF THE INVENTION

The following description is of the best mode presently contemplated forcarrying out at least one embodiment of the invention. This descriptionis not to be taken in a limiting sense, but is made merely for thepurpose of describing the general principles of the invention. The scopeof the invention should be determined with reference to the claims.

With reference to FIG. 1, an example embodiment of a system 100according to the invention is shown within a networked environment. Thenetworked environment includes a plurality of data processing devices,consisting of both mobile and static data processing terminals 105 _(N),110 _(N) each capable of at least data communication with one anotheracross a network including a Wide Area Network (‘WAN’) 111 such as theWorld Wide Web or Internet.

In the example, each mobile data communication device 105 _(N) is amobile telephone handset 105 having wireless telecommunication emittingand receiving functionality over a cellular telephone network configuredaccording to the Global System for Mobile Communication (‘GSM’), GeneralPacket Radio Service (‘GPRS’), International MobileTelecommunications-2000 (IMT-2000, ‘W-CDMA’ or ‘3G’), InternationalMobile Telecommunications-Advanced, (ITU-R-compliant and known as ‘4G’)network industry standards, and wherein telecommunication is performedas voice, alphanumeric or audio-video data using the Short MessageService (‘SMS’) protocol, the Wireless Application protocol (‘WAP’) theHypertext Transfer Protocol (‘HTTP’) or the Secure Hypertext TransferProtocol (‘HTTPS’).

Each mobile telephone handset 105 _(N) receives or emits voice, text,audio and/or image data encoded as a digital signal over a wireless datatransmission 106, wherein the signal is relayed respectively to or fromthe handset by the geographically-closest communication link relay 107of a plurality thereof. The plurality of communication link relays 107allows digital signals to be routed between each handset 105 and theirdestination by means of a remote gateway 108 via a MSC or base station109. Gateway 108 is for instance a communication network switch, whichcouples digital signal traffic between wireless telecommunicationnetworks, such as the cellular network within which wireless datatransmissions 106 take place, and the Wide Area Network 111. The gateway108 further provides protocol conversion if required, for instancewhether a handset 105 uses the WAP or HTTPS protocol to communicatedata.

Alternatively, or additionally, one or more of the plurality of mobiledata communication device 105 _(N) may have wired and/or wirelesstelecommunication emitting and receiving functionality over,respectively, a wired Local Area Network (‘LAN’) and/or a wireless localarea network (‘WLAN’) conforming to the 802.11 standard (‘Wi-Fi’). Inthe LAN or WLAN, telecommunication is likewise performed as voice,alphanumeric and/or audio-video data using the Internet Protocol (IP),Voice data over IP (‘VoIP’) protocol, Hypertext Transfer Protocol(‘HTTP’) or Secure Hypertext Transfer Protocol (‘HTTPS’), the signalbeing relayed respectively to or from the mobile data communicationdevice 105 by a wired (LAN) or wireless (WLAN) router 109 interfacingthe mobile data communication device 105 to the WAN communicationnetwork 111. A mobile telephone handset 105 may have wirelesstelecommunication emitting and receiving functionality over the WLAN inaddition to GSM, GPRS, W-CDMA and/or 3G, ITU-R/4G.

A typical handset 105 _(N) for use with the system according to theinvention is preferably that commonly referred to as a ‘smartphone’ andmay for instance be an iPhone™ handset manufactured by the AppleCorporation or an equivalent handset configured with the Android™operating system provided by Google, Inc. Generally, the mobile terminal105 may be any portable data processing device having at least wirelesscommunication means and audio recording and storage means. It willtherefore be readily understood by the skilled person from the presentdisclosure, that one or more of the mobile data communication devices105 may instead be a portable computer commonly referred to as a‘laptop’ or ‘netbook’, a tablet computer such as an Apple™ iPad™ or aSamsung™ Galaxy™, and the like.

Accordingly, a typical hardware architecture of a mobile telephonehandset 105 _(N) is shown in FIG. 2 in further detail, by way ofnon-limitative example. The handset 105 firstly includes a dataprocessing unit 201, for instance a general-purpose microprocessor(CPU), acting as the main controller of the handset 105 and which iscoupled with memory means 202, comprising non-volatile random-accessmemory (‘NVRAM’).

The mobile telephone handset 105 further includes a modem 203 toimplement the wireless communication functionality, as the modemprovides the hardware interface to external communication systems, suchas the GSM or GPRS cellular telephone network 107, 108 shown in FIG. 1.An aerial 204 coupled with the modem 203 facilitates the reception ofwireless signals from nearby communication link relays 107. The modem203 is interfaced with or includes an analogue-to-digital converter 205(‘ADC’) for demodulating wavelength wireless signals into digital data,and reciprocally for outgoing data.

The handset 105 further includes self-locating means in the form of aGPS receiver 206, wherein the ADC 205 receives analogue positional andtime data from orbiting satellites (not shown), which the dataprocessing unit 201 or a dedicated data processing unit processes intodigital positional and time data.

The handset 105 further includes a sound transducer 207, for convertingambient sound waves, such as the user's voice into an analogue signal,which the ADC 205 receives for the data processing unit 201 or adedicated data processing unit to process into digital first audio data.

The handset 105 may optionally further include imaging means 208 in theform of an electronic image sensor, for capturing image data which thedata processing unit 201 or a dedicated data processing unit processesinto digital image data.

The CPU 201, NVRAM 202, modem 203, GPS receiver 206, microphone 207 andoptional digital camera 208 are connected by a data input/output bus209, over which they communicate and to which further components of thehandset 105 are similarly connected, in order to provide wirelesscommunication functionality and receive user interrupts, inputs andconfiguration data.

Alphanumerical and/or image data processed by CPU 201 is output to avideo display unit 210 (‘VDU’), from which user interrupts may also bereceived if it is a touch screen display. Further user interrupts mayalso be received from a keypad 211 of the handset, or from an externalhuman interface device (‘HiD’) connected to the handset via a UniversalSerial Bus (‘USB’) interface 212. The USB interface advantageously alsoallows the CPU 201 to read data from and/or write data to an external orremovable storage device. Audio data processed by CPU 201 is output to aspeaker unit 213.

Power is provided to the handset 105 by an internal module battery 214,which an electrical converter 215 charges from a mains power supply asand when required.

The system 100 next includes one or more data processing terminals 110_(N), each of which emits and receives data encoded as a digital signalover a wired data transmission conforming to the IEEE 802.3 (‘GigabitEthernet’) standard, wherein the signal is relayed respectively to orfrom the computing device by a wired router 109 interfacing thecomputing device 110 to the WAN communication network 111. Generally,each data processing terminal 110 _(N) may be any portable or desktopdata processing device having at least networking means apt to establisha data communication with the plurality of mobile data communicationdevices 105 _(N).

A typical hardware architecture of a data processing terminals 110 _(N)is shown in FIG. 3 in further detail, by way of non-limitative example.The data processing device 110 is a computer configured with a dataprocessing unit 301, data outputting means such as video display unit(VDU) 302, data inputting means such as HiD devices, commonly a keyboard303 and a pointing device (mouse) 304, as well as the VDU 302 itself ifit is a touch screen display, and data inputting/outputting means suchas the wired network connection 305 to the communication network 111 viathe router 109, a magnetic data-carrying medium reader/writer 306 and anoptical data-carrying medium reader/writer 307.

Within data processing unit 301, a central processing unit (CPU) 308provides task co-ordination and data processing functionality. Sets ofinstructions and data for the CPU 308 are stored in memory means 309 anda hard disk storage unit 310 facilitates non-volatile storage of theinstructions and the data. A wireless network interface card (NIC) 311provides the interface to the network connection 305. A universal serialbus (USB) input/output interface 312 facilitates connection to thekeyboard and pointing devices 303, 304.

All of the above components are connected to a data input/output bus313, to which the magnetic data-carrying medium reader/writer 306 andoptical data-carrying medium reader/writer 307 are also connected. Avideo adapter 314 receives CPU instructions over the bus 313 foroutputting processed data to VDU 302. All the components of dataprocessing unit 301 are powered by a power supply unit 315, whichreceives electrical power from a local mains power source and transformsit according to component ratings and requirements.

In the example, any user operating a mobile terminal 105 ₂, 105 ₄ orstatic terminal 110 ₂ may access the website of an entity 101 providingcommercial goods or services for obtaining information about the entityand/or its goods or services. The entity 101 accordingly operates astatic terminal 110 ₁ configured as a web server for distributing thewebsite to requesting remote terminals 105 ₂, 105 ₄ and/or 110 ₂, and atleast one interlocutor at the entity operates a mobile terminal 105 ₁for meeting any ad hoc telecommunication requirements of their users.The entity 101 also has the use of one or more interlocutors at a remotecall center entity 102, which operates both a static terminal 110 ₃ anda mobile terminal 105 ₃ for telecommunication support and assistance.

FIG. 4 details the data processing steps of an embodiment of the method,performed in the environment of FIGS. 1 to 3 with the user dataprocessing terminal 105 ₂ and the entity data processing terminals 110 ₁and 105 ₁.

In a simple embodiment of the method, a local user application is servedby the entity server 110 ₁ to the user mobile terminal 1052 accessingthe entity website, which exposes at least one Application ProgrammerInterface (‘API’) passing the user input data representative of the userinteractions to an analyzing and routing application hosted by theserver 110 ₁. When the mobile terminal 105 ₂ accesses the website at theserver PC 110 ₁, the API is loaded and then captures and communicatesuser interaction data to the server 110 ₁ substantially in real time.The server application analyses the communicated captured data formatching the user's interests, as derived from the analyzed interactiondata, with one or more interlocutors associated with the entity, who isor are most apt to interact with the user in any real-timetelecommunication which may follow the user's interaction with thewebsite, e.g. a specialist interlocutor with relevant training in suchderived interests. Whenever the user should initiate a real-timetelecommunication, for instance with selecting a ‘call’, ‘chat’,‘videoconferencing’ (and equivalents) button in a webpage, the serverapplication routes any such telecommunication to the terminal 105 ₁ or105 ₃ or 110 ₃ of the matched interlocutor, preferably with the analyzedand/or captured data.

A real-time telecommunication between a user and a matched interlocutorwill be routed as a media path using any of alphanumerical (e.g. InstantMessaging), voice-only (e.g. telephone) or voice & video (e.g. Skype™)formats, within any of the VoIP, WebRTC or PSTN network structuresdepending how the user is accessing the website (e.g. desktop or laptopPC 110 ₂, tablet or smartphone 105 ₂).

Accordingly, configuration parameters including interlocutor data,telecommunication formats and routing options and rules should first beinput at the server 110 ₁ into a database processed by the serverapplication at step 401. Interlocutor data will comprise characteristicsfor each interlocutor, such as name, areas of training and/or topicalspecialty, keywords and other logical and/or semantic distinguishingparameters, as well as respective terminal type, communication andfunctional capabilities in respect of the above formats and structures,an importantly at least one network address for each interlocutorterminal. As many such interlocutor records may be input as there arepotential interlocutors for a calling user, and recorded interlocutorsmay receive further training to expand their telecommunication aptitude,whereby new interlocutor records may be instantiated, and current newinterlocutor records may be updated at step 402, whereby controllogically returns to step 401.

In parallel with the above, at any given time, a user at a terminal 105₂, 110 ₂ may access the server 110 ₁ for loading and perusing the entitywebsite in a new browsing session at step 403. The server 110 ₁accordingly creates a new user session record in the database at step404, and makes access to the website resources conditional upon a loginauthentication step at step 405. The user subsequently inputs relevantauthentication input data at terminal 105 ₂ at step 406, for instance auser name and/or login credential data of an online social mediaresource. Such authentication input data is forwarded to the server 110₁ at step 407, at which it is recorded in the user session record.

The server 110 ₁ may optionally make access to the website resourcesconditional upon a secondary identity verification step at step 408. Theuser subsequently inputs relevant identity input data at terminal 105 ₂at step 409, for instance a first name at least. Such identityverification input data is again forwarded to the server 110 ₁ at step410, at which it is again recorded in the user session record.

The user subsequently accesses and peruses the website resources,including any or all of website data, website metadata, webpage pagetags, website cookies, website page headers, user social media datathrough conventional user page and link selections, at step 411. Theuser thus generates a stream of selections with e.g. mouse 304, known asa clickstream, and may also input one or more search strings forlocating website resources. The API captures and communicates thisinteraction data substantially in real-time at step 411, whether asinteraction event-driven updates or in aggregate form, for instances asa website navigation record representative of historical or sequentialclicks, or as a combination of both in dependence on bandwidth or loadconstraints.

The communicated captured interaction data is received by the serverapplication at server 110 ₁ and analyzed to step 412 using variousstatistical predictive analytics techniques such as Bayesian inferencingor regression models that are known in the art. The user's interactionpattern is determined by the server application and an interactionprediction is output, such as when a customer will initiate atelecommunication call, or may cease to peruse the webpage or website.The interaction prediction is used to trigger a matching operation atstep 413 of the user with a respective recorded interlocutor, which isaccomplished by comparing the interaction pattern with interlocutorparameters, in particular areas of training and/or topical specialty andkeywords, and determining the recorded interlocutor with the closestrecord correlating the interaction pattern.

At step 414, the matching interlocutor record is selected and therouting path between the user and the selected interlocutor isdetermined based on the network address of the interlocutor terminalrecorded in the database whereby, if the user should initiate areal-time telecommunication call from the website substantially at thattime according to the prediction at step 415, the server 110 ₁ receivesthe corresponding event call message at step 416 and the serverapplication routes the call to the selected interlocutor at step 417,together with either the user's interaction pattern, or the captured andcommunicated user interaction data, or both.

The server application at the server 110 ₁ is a multi-threadedapplication apt to perform each of steps 401, 402,m 404, 407, 410, 412to 414 and 416, 417 substantially concurrently for a plurality ofinteracting users and a plurality of interlocutors, and steps 412 to 414themselves form a looping subroutine of the application whereby, shoulda user not initiate a predicted telecommunication call at step 415, asubsequent captured and communicated interaction of the user translatinga shifting point of interest would be processed at a next iteration ofsteps 412 to 414 and optimally result in potentially a different matchedinterlocutor with a respective different, but more relevant, set ofparameters correlating with the shifted point of interest.

It will be appreciated that the agent (Website hosts. App providers) cansetup the positive and negative Outcomes they wish toachieve/avoid—simple ones being “Purchase” or “Support Ticket Closure”,more complex ones being “Openness to Sales engagement”. The inventionprovides a Machine Learning component can determine which customers whenoffered Calls, Chat, Emails, Web Page Suggestions are more likely tocomplete the desired Outcome. The machine learning component or modulecaptures the data and is analyzed based on a pattern and used to predicta successful outcome between a first user and selected second user. Inone embodiment the machine learning component or module comprises aclustering technique to cluster specific patterns of captured data andstore over time for comparison with new captured data.

The Outcome is captured in a binary fashion at the end of each call orsession by either the Agent or by the system retrieving the Outcomeprogrammatically from a CRM or Ticketing system that the agent enteredthe Outcome into or upon user completion of a sequence of actionsdefining an outcome. This forms the feedback loop for the system tounderstand persona/activity patterns that drive good Outcomes vs. badOutcomes.

Based on this mechanism the system can offer Chats and Calls toCustomers most likely to convert who would have otherwise not converted.This is important, as the pool of Agents is always limited. ThereforeAgent time is geared to calls, chats (Interactions) with customers thathave the best likelihood to maximize the defined Outcome.

If an agent or business does not initially want to allow the system tomake the actual call/chat offers then the Machine Learning can be usedto present ranked lists of Customers and/or actions to the Agent for aspecific Outcome who can then be manually offered a call or a chat atthe discretion of the Agent.

FIG. 5 is a block diagram illustration of a high-level implementation ofthe method of FIG. 4 within the system 100 in the environment of FIGS. 1to 3 at runtime.

The steps of the method may usefully be implemented in the system 100 asa user application 501 processed in conjunction with the browser of auser mobile terminal 105 ₂, 105 ₄, which exposes one or more APIs to thewebsite of the entity 101; a server application 502 processed by theentity terminal 110 ₁ with an associated, highly scalable database; andan interlocutor application, at its simplest a browser processed by themobile or static terminal 110 ₃, 105 ₁, 105 ₃ of the interlocutor towhom a telecommunication initiated by the user is routed by the serverapplication 502 that can be interpreted as an interactive managementengine.

The user application 501 captures website data 504, such as cookies, webpage headers and the like as the user interacts therewith on theirmobile terminal 105 ₂, besides the social media data 505 gathered whenthe user logs into the website using social media-based authenticationat step 406 and terminal data 506 indicative of at least the terminaltype, and sends the captured data to the server application 502 per step411.

The server application 502 is an analytics and routing applicationcomprising a database, an analytics module 507, a routing module 508 anda parameterizing module 509, and which processes the captured user datait receives for identifying a relevant interlocutor associated with theentity 101 deemed most relevant, and routing an eventualtelecommunication of the user thereto. The server application performssteps 401, 402, 404, 405, 407, 408, 410, 412 to 414 and 416, 417.

The parameterizing module 509 specifically performs steps 401 and 402,i.e. it is used for defining the attributes of each interlocutor inrelation to the website data in logical and e.g. semantic terms, theattributes of interlocutor terminals 110 ₃, 105 ₁, 105 ₃ and theattributes of telecommunication links with user terminals 105 ₂, 105 ₄and such as terminal type, associated communication functionalities,minimum bandwidth levels for ensuring Quality of Service percommunication type.

The analytics module 507 specifically performs steps 412 to 414, i.e. itreceives all of the user data captured by the user application 501, thusincluding e.g. website data, website metadata, webpage page tags,website cookies, website page headers, one or more clickstream, one ormore search strings, a website navigation record representative ofhistorical or sequential user page and link selections, and user socialmedia data.

The routing module 508 specifically performs steps 416 and 417, i.e. itis triggered by a telecommunication call event and routes the user callto the selected interlocutor terminal 105 ₁,105 ₃, 110 ₃ with the outputof the analytics module 507 and optionally also the captured userinteraction data received pursuant to step 411.

FIG. 6 is a logical diagram of the contents of the memory means 202 ofthe user mobile terminal 105 ₂ as shown in FIGS. 1 and 2, whenperforming the method of FIG. 4 at runtime, including a website in abrowser, a first set of instructions and captured interaction data asillustrated in FIG. 5. It will be readily understood by the skilledperson that the foregoing is applicable to the alternative memory means309 of a user static terminal 110 ₂ as shown in FIGS. 1 and 3, whenperforming the method of FIG. 4 at runtime.

An operating system is firstly shown at 601 which, depending on thehandset manufacturer, may be an iOS version developed and distributed byApple Inc. or Android™ developed and distributed by Google Inc. A subsetof instructions 602 of the OS 601 is dedicated to communicationprocessing, in particular data communications with remote terminals 105_(N), 110 _(N) within the network environment of FIG. 1.

A browser application is shown at 603, which configures the mobilehandset 105 ₂ to access, process and display the website resourceshosted by the remoter entity server 110 ₁. A set of instructions 604corresponds to the user application 501 and is dedicated to implementingthe functionality of steps 406, 409 and 411 as previously described andwhich is interfaced with the browser application 602 and the networklayer of the OS 601. The memory 202 may comprise one or more furtherapplications being processed in parallel with the browser and API 603,604 at runtime, that is or are unrelated to the present invention andgenerally shown at 605.

Browser application data is shown at 606, which comprises accessedwebsite resources 607 downloaded from the remote server 110 ₁ and agraphical user interface 608 output to the display 210 and into whichthe downloaded resources are rendered.

User application data is shown at 609, which comprises the capturedwebsite data, website metadata, webpage page tags, website cookies,website page headers, one or more clickstream, one or more searchstrings, a website navigation record representative of historical orsequential user page and link selections, user social media data and thelike being sent to the remote server application 502 at the remoteserver 110 ₁.

The memory 202 may further comprise conventional local and/or networkdata that is unrelated to the browser and API 603, 604, for instanceused by the one or more further applications 605 and/or the OS 601 andgenerally shown at 610.

FIG. 7 is a logical diagram of the contents of the memory means of thedata processing terminal 110 ₁ shown in FIGS. 1 and 3, when performingthe method of FIG. 4 as a server at runtime, including the first set ofinstructions and captured interaction data, a second set ofinstructions, analyzed interaction data and a database, as illustratedin FIGS. 5 and 6.

An operating system is shown at 701 which, if the terminal 110 ₁ is adesktop computer, is for instance Windows 7™ distributed by theMicrosoft Corporation. The OS 701 includes communication subroutines 702to configure the terminal for bilateral network communication in thenetwork environment of FIG. 1 via the NIC 311, then the router 109.

The server application 502 is shown at 703, which configures theterminal 110 ₁ to perform at least processing steps 401, 402, 404, 405,407, 408, 410, 412 to 414 and 416, 417 as described hereinbefore, andwhich is interfaced with the OS 601 and network communicationsubroutines 602 thereof via one or more suitable Application ProgrammerInterfaces. Accordingly, the server application includes the analyticsmodule 507, the routing module 508 and the parameterizing module 509 aspreviously described, and is therefore apt to request and obtainauthentication data, identity verification data and captured interactiondata 609 from each remote user accessing the website resources 607.

A database is accordingly shown at 704, in which the website resources607 are stored, as well as terminal types and capacities 705 forinterlocutors and users, telecommunication rules 706 and interlocutorprofiles 707 as established at steps 401, 402, and the user sessionrecords comprising the captured interaction data 609 for each remoteuser accessing the website resources 607.

Server application data specific to the analytics module 507 is shown at708, which includes user analyzed data 709 such as interaction patternsprocessed at step 412 and matched user-interlocutor pairs 710 pursuantto step 413.

Server application data specific to the routing module 508 is shown at711, which includes user terminal—interlocutor terminal communicationpaths 712 output by the analytics module 507 for perusing by the routingmodule.

The memory 309 may further comprise conventional local and/or networkdata that is unrelated to the server application 703, for instance usedby the one or more further applications and/or the OS 601 and generallyshown at 713.

FIG. 8 is a logical diagram of the contents of the memory means 202 ofan interlocutor mobile terminal 105 ₁, 105 ₃ as shown in FIGS. 1 and 2,when performing the method of FIG. 4 at runtime, including a website ina browser and analyzed interaction data as illustrated in FIGS. 5 to 7.It will be readily understood by the skilled person that the foregoingis applicable to the alternative memory means 309 of an interlocutorstatic terminal 110 ₃ as shown in FIGS. 1 and 3, when performing themethod of FIG. 4 at runtime.

An operating system is firstly shown at 601 which, depending on thehandset manufacturer, may be an iOS version developed and distributed byApple Inc. or Android™ developed and distributed by Google Inc. A subsetof instructions 602 of the OS 601 is dedicated to communicationprocessing, in particular data communications with remote terminals 105_(N), 110 _(N) within the network environment of FIG. 1.

A browser application is shown at 603, which configures the mobilehandset 105 ₂ to process and display a distributed or local call supportapplication. The memory 202 may comprise one or more furtherapplications being processed in parallel with the browser 603 atruntime, that is or are unrelated to the present invention and generallyshown at 605.

Browser application data is shown at 606, which comprises downloadedsupport application resources 801 and a graphical user interface 608output to the display 210 and into which the downloaded resources arerendered.

In the case of the interlocutor terminal, in this embodiment the browserapplication data also comprises analyzed user data, which consists ofthe output of the server application communicated to the interlocutorterminal 105 ₃ at step 417. In an alternative embodiment (not shown) theremote server 110 ₁ may also forward, or forward instead, thus thebrowser application data of the interlocutor terminal 105 ₃ may furtheror alternatively comprise, some or all of the captured user data 609received by the remote server 110 ₁ prior to analysis. In either case,the analyzed and/or captured data is forwarded by the remote server 110₁ for ad hoc referencing purposes or otherwise, so that the interlocutorhas as much call supporting information at hand as is available in orderto facilitate the telecommunication with the calling user.

The memory 202 may again further comprise conventional local and/ornetwork data that is unrelated to the browser 603, for instance used bythe one or more further applications 605 and/or the OS 601 and generallyshown at 610.

The present invention thus provides a computer-implemented method and acorresponding system comprising means for communicating at least one APIto one or more network resources, typically a website or a mobileapplication site; means for routing interactions from each useraccessing and browsing the or each network resource to an interlocutor,or a group thereof, of the entity respectively associated with the oreach network resource, typically a company or other goods or serviceprovider; and at least one API configured to allow the passing ofinteraction data gathered as the user accesses the network resource,optionally through a social media-based authentication procedure, thenbrowses it. This gathered data is used by the routings means, a typicalembodiment of which may be an analytics routing application processed bya server, in order to select the interlocutor or group thereof with theattributes most closely corresponding to the anticipated requirements ofthe user at the time the user initiates a telecommunication.

Many implementing variations may be devised by the skilled person independence upon requirements of increased functionality, simplicity ofimplementation, availability of data processing and network bandwidth,scalability and more. Accordingly, there follows comments about, andbrief descriptions of, alternative embodiments, all of which areprovided by way of non-limitative example and readily associated withthe environment of FIGS. 1 to 3 at least.

The routing functionality, in its simplest form, may rely upon meta-datakeywords associated with the originating webpage of a website, i.e. thewebpage on which a ‘Click to Call’ or ‘Chat’ user-selected button hasbeen selected with the pointing device 304, and the ensuingtelecommunication routed to one or a group of interlocutors that havepreferably been associated with those keywords by a user of the server110 ₁.

Similarly, such a server 110 ₁ can associate one or more interlocutorswith search terms that commonly are entered in the website, afteranalyzing historical search data input to the server. An implementationroutine may for instance monitor each website search event followed bythe initiation of an interaction request from the search results page bya remote user accessing the website.

The system 100 may likewise use the output of the customer behavioraldata analysis 506, such as browsing history or click stream, to augmentkeyword-based routing and improve the accuracy of routing interactionsto the most suitable agents. This is achieved through the followingmechanisms.

A server user can define, with the parametering module 508, a set ofhard routing constraints that associate selected meta-data keywords ofthe originating website page and the interlocutor skillsets. Suchmappings will ensure particular routing constraints, for example thattelecommunications originating from a web page in French will be routedto a French-speaking interlocutor.

In addition, the system 100 continuously collects user behavioral datafor each web session through tracking beacons (JavaScript snippetsembedded in the website, similarly to Google Analytics) that are passedto the user-facing API. This data, including web pages, which the uservisited prior to making the call, is used to identify categories ofusers (persona clusters) and telecommunication steps (journey patterns)with a chosen clustering algorithms (e.g. Support Vector Machines thatare known in the art). This information when also rendered on aninterlocutor desktop application 801 provides valuable information onuser activity prior to the call.

The system 100 automatically assigns the telecommunication to one of thecomputed categories and identifies in real-time the most accuraterouting decisions using prior knowledge (e.g. success of eachinterlocutor with similar calls) that can be implemented using a numberof typical supervised machine-learning approaches relevant in thisscenario.

Similarly, user behavioral data that may include user actions orlocation is also gathered for mobile app users through the mobilebeacons mechanism and the user-facing API. The collected information isused similarly to the web session data, allowing identification ofpersona clusters and journey patterns with selected machine learningtechniques. Historical telecommunication data is used to enhance routingdecisions based on the success of previous routing decisions. It is alsopresented on an interlocutor desktop application 801 to provide morecontextual information about the call to the interlocutor.

Location services can resolve IP addresses into relatively accuratestreet addresses and GPS co-ordinates obtained via the GPS module 206can be mapped using web services like Google Maps among others. Thisinformation when rendered on an interlocutor desktop application 801provides information to an interlocutor for resolving categories ofenquiry from users that require knowing where a user is geographicallyrelative to services, field personnel or premises.

Users may choose to make their social data available to the system byauthenticating on the website per step 405, 406 or when initiating thecall. Social data is invaluable for personalization throughidentification of user demographics, interests and likes. The system 100can use this information to route the user to the most suitableinterlocutor, but also to compute more accurate recommendations, such ason call up-sell recommendations displayed in the interlocutor desktopapplication 801.

The invention accordingly allows use of the rich contextual data fromthe actual website page browsing and activities carried out by the useras the inputs to determine the routing of the telecommunication andanalysis of this data to provide further values such as pre-emptiveoffering of interlocutor assistance at critical points in the userjourney and recommendations to the interlocutor to increase the value ofthe telecommunication.

Meta keywords (e.g. “<meta name=“keywords” content=“Sales, CarInsurance”/>”) of the web page from which the user originated thetelecommunication is used to map the telecommunication to a set ofinterlocutors assigned to handle telecommunications associated withspecific keywords.

Search terms input by the user prior to initiating the telecommunicationare used to route the telecommunication to the set of interlocutorsassociated with those search terms. Data collected from previousbrowsing activity and telecommunications may also be used to categorizethe user and route the telecommunication to the interlocutor predictedto be the best fit for a given category. Data identifying a website pagethat the user has browsed on the website is passed via an API to thesystem and this browse sequence may be used to route thetelecommunication to an interlocutor compared to historical datarepresentative of the interlocutors who were rated best to handle thetelecommunication request by previous users.

An analysis of the browsing activities by a user can predictivelydetermine that the user will call and can therefore begin queuing atelecommunication in the system in anticipation of this call, so thatthe user is answered more quickly when a call is initiated by a user. Inone embodiment based on the aggregation and analysis of user browsingpatterns, a call can be offered to a user with a pre-reservedinterlocutor at a critical point in a user journey through the websiteresources 607 to optimize user satisfaction.

Referring again to FIG. 5 in one embodiment of the invention thecaptured data is analyzed based on a pattern and used to predict asuccessful outcome between the first user and a selected second user,i.e. the interlocutor or agent acting for the website being browsed bythe first user, by the analytics module 507. The invention utilizes aset of interaction management processes that takes a set of inputs thatinclude raw user online data (website data, behavior data, demographicsdata), hard constraints configuration (agent and queue assignment) andanalytics data in various forms (e.g. smart attributes derived based onthe analysis of raw data using machine learning, such as need orlikelihood to achieve a specific outcome (which can change dynamicallyduring a customer journey), such as purchase a product, book a demo,require support, likelihood of accepting an interaction offer) in orderto make interaction management decisions that can include:identification of customer most valuable within a persona set given anoutcome to maximize, smart call routing based on agent efficiencyclusters, interaction recommendations to agents (what type ofinteraction—chat/audio/video—should be offered to which users and when),as well as automatic interaction offers focused on shaping customerjourney and optimizing the business outcomes.

In one embodiment the system takes the following inputs:

1. Raw user behavior data—such as mobile data, social data

2. Hard constraints configurations—rules ensuring that particularbusiness-defined constraints are met. Examples may include call routingor interaction offer based on user online data.

3. A set of Outcomes that the business wants to maximize, which aredefined as a set of actions executed by the customer, e.g. purchase aproduct, book a demo or complete a form.

4. Analytics data constitutes the key input for the system. Examples ofsuch data includes: smart attributes (e.g. need for support, propensityto complete an outcome) derived based on raw user data using machinelearning algorithms, persona clusters that dynamically group users basedon their behavior and demographics, as well as journey patternsrepresenting similar set of sequences of user actions.

The analytics module 507 takes the inputs and processes the data usingthe analytics data. The outputs can be Machine Learned “smartattributes” ranking customers against Outcomes defined by the businessand within Persona Clusters assigned by the Machine Learning orspecified by the business associated with the website.

The outputs can be interaction recommendations. The routing module 508is configured to make interaction recommendations to agents providingthe agent with contextual data learned about the users (e.g. likelihoodof accepting an interaction offer, likelihood of purchasing a product)based on inputs provided. For example, analytics module 507 can identifythe users that the agent should offer chat interactions to in order toincrease product sales. This allows agents to focus on users orcustomers with predicted high value with respect to a given outcome,thus optimizing the resource utilization.

The outputs can be automatic interaction offers. The analytics module507 can make informed decisions and automatically offer interaction toselected users. Such interaction offers are made based on the analyticsdata in order to optimize the business-defined outcomes. First, usingthe input data (as described above) the component identifies the set ofcustomers that are of high value with respect of a given outcome.Appropriate treatment (i.e. interaction offer) is then identified usingpredictive models built using various machine learning algorithmsincluding decision trees, Bayesian inferencing and neural networks. Theanalytics module 507 can be configured to determine how many interactionoffers should be automatically made to customers in order to maximizeagent utilization and minimize users' or customers' queuing time.

The routing module 508 makes routing decisions and performs agentselection based on inputs provided (as outlined above) using machinelearned inference based on persona attributes (age, location, role,gender, etc.) and activities (page views, calls, chat, etc.)—Customerswith similar personae and activities patterns do better with Agents likeAgent X, customer X is should have an agent reserved as they are aboutto make a call or the system has offered them an interaction and theyare likely to accept.

The embodiments in the invention described with reference to thedrawings comprise a computer apparatus and/or processes performed in acomputer apparatus. However, the invention also extends to computerprograms, particularly computer programs stored on or in a carrieradapted to bring the invention into practice. The program may be in theform of source code, object code, or a code intermediate source andobject code, such as in partially compiled form or in any other formsuitable for use in the implementation of the method according to theinvention. The carrier may comprise a storage medium such as ROM, e.g.CD ROM, or magnetic recording medium, e.g. a floppy disk or hard disk.The carrier may be an electrical or optical signal, which may betransmitted via an electrical or an optical cable or by radio or othermeans.

Additionally, at least a portion of the systems, methodologies andtechniques described with respect to the exemplary embodiments ofpresent disclosure can incorporate a machine, such as, but not limitedto, computer system, or any other computing device within which a set ofinstructions, when executed, may cause the machine to perform any one ormore of the methodologies or functions discussed above. The machine maybe configured to facilitate various operations conducted by the systemsdisclosed herein. For example, the machine may be configured to, but isnot limited to, assist the systems by providing processing power toassist with processing loads experienced in the systems, by providingstorage capacity for storing instructions or data traversing thesystems, or by assisting with any other operations conducted by orwithin the systems.

Dedicated hardware implementations including, but not limited to,application specific integrated circuits, programmable logic arrays andother hardware devices can likewise be constructed to implement themethods described herein. Applications that may include the apparatusand systems of various embodiments broadly include a variety ofelectronic and computer systems. Some embodiments implement functions intwo or more specific interconnected hardware modules or devices withrelated control and data signals communicated between and through themodules, or as portions of an application-specific integrated circuit.Thus, the example system is applicable to software, firmware, andhardware implementations.

In accordance with various embodiments of the present disclosure, themethods described herein are intended for operation as software programsrunning on a computer processor. Furthermore, software implementationscan include, but are not limited to, distributed processing orcomponent/object distributed processing, parallel processing, or virtualmachine processing can also be constructed to implement the methodsdescribed herein.

In the context of the present invention it will be appreciated that theterm ‘website’ should be provided a broad interpretation to includemobile application sites, apps downloaded onto a terminal and capable ofcommunicating with a portal or IP address or any communication meansthat facilitates communication over a network.

In the specification the terms “comprise, comprises, comprised andcomprising” or any variation thereof and the terms include, includes,included and including” or any variation thereof are considered to betotally interchangeable and they should all be afforded the widestpossible interpretation and vice versa.

The invention is not limited to the embodiments hereinbefore describedbut may be varied in both construction and detail.

It will be apparent to those skilled in the art that numerousmodifications and variations of the described examples and embodimentsare possible in light of the above teaching. The disclosed examples andembodiments are presented for purposes of illustration only. Otheralternate embodiments may include some or all of the features disclosedherein. Therefore, it is the intent to cover all such modifications andalternate embodiments as may come within the true scope of thisinvention.

What is claimed is:
 1. A method for capturing and analyzingpattern-based data and using the analysis to predict a desired outcomebetween a first user and selected second user from a plurality of users,wherein the selected second user is affiliated with a contact centerassociated with a website being browsed by the first user, the methodcomprising: collecting a set of inputs around the browsing of the firstuser of the website via an Application Programming Interface, whereinthe Application Programming Interface transmits the set of inputs to adata processor; analyzing the set of inputs by the data processor,wherein the analyzing further comprises: the application of machinelearning to the set of inputs to derive attributes, assigning a personacluster to the first user that dynamically groups the first user with aplurality of other users based on browsing behavior and demographics,and examining journey patterns representing a similar set of sequencesof other user actions to the first user; and performing a managementdecision on the interaction based on the set of inputs analyzed toachieve the desired outcome.
 2. The method of claim 1, wherein theinputs comprise at least one of: raw data of online activity by thefirst user, hard constraints configurations of the contact center, a setof outcomes a business affiliated with the website wants to maximize,and analytics data.
 3. The method of claim 2, wherein the raw datacomprises one or more of: website data, behavior data, demographic data,mobile data, and social data.
 4. The method of claim 2, wherein the hardconstraints configurations comprise one or more of: second user andqueue assignment, interaction routing based on data of the first user,interaction offers based on data of the first user, and rules.
 5. Themethod of claim 2, wherein the set of outcomes comprise a set of actionsexecuted by the first user.
 6. The method of claim 1, wherein themachine learning algorithms create outputs of attributes ranking theplurality of second users against outcomes defined by the contact centerand within the persona clusters.
 7. The method of claim 1, wherein themanagement decision comprises identification of the first user as avaluable user within a persona set given an outcome to maximize.
 8. Themethod of claim 1, wherein the management decision comprises smartinteraction routing based on agent efficiency clusters.
 9. The method ofclaim 1, wherein the management decision comprises interaction typerecommendations to the selected second user to offer to the first userand offer timing.
 10. The method of claim 1, wherein the managementdecision comprises automatic interaction offers to the first userfocused on shaping customer journey.
 11. The method of claim 1, whereinthe management decision comprises automatic interaction offers to thefirst user focused on optimizing outcome.
 12. The method of claim 1,wherein the desired outcome comprises an outcome the contact centerwants to avoid.
 13. The method of claim 1, wherein the outcome iscaptured in a binary fashion at the end of the interaction and formedinto a feedback loop for pattern understanding used in the machinelearning.
 14. The method of claim 1, wherein the analyzing furthercomprises determining a ranked list for the desired outcome, wherein theranked list comprises which action to offer the first user by a selectedsecond user to complete the desired outcome.
 15. The method of claim 14,wherein the action comprises at least one of: a voice call, a chat,email, and web page suggestions.
 16. A system for capturing andanalyzing pattern-based data and using the analysis to predict a desiredoutcome between a first user and selected second user from a pluralityof users, wherein the selected second user is affiliated with a contactcenter associated with a website being browsed by the first user, thesystem comprising: a processor; and a memory in communication with theprocessor, the memory storing instructions that, when executed by theprocessor, causes the processor to route the interaction by: analyzing acollected set of inputs around the browsing of the first user of thewebsite, wherein the analyzing further comprises: the application ofmachine learning to the set of inputs to derive attributes, assigning apersona cluster to the first user that dynamically groups the first userwith a plurality of other users based on browsing behavior anddemographics, and examining journey patterns representing a similar setof sequences of other user actions to the first user; and performing amanagement decision on the interaction based on the set of inputsanalyzed to achieve the desired outcome.
 17. The system of claim 16,wherein the desired outcome comprises an outcome the contact centerwants to avoid.
 18. The system of claim 16, wherein the outcome iscaptured in a binary fashion at the end of the interaction and formedinto a feedback loop for pattern understanding.
 19. The system of claim16, wherein the inputs comprise at least one of: raw data of onlineactivity by the first user, hard constraints configurations of thecontact center, a set of outcomes a business affiliated with the websitewants to maximize, and analytics data.
 20. The system of claim 19,wherein the hard constraints configurations comprise one or more of:second user and queue assignment, interaction routing based on data ofthe first user, interaction offers based on data of the first user, andrules.
 21. The system of claim 19, wherein the set of outcomes comprisea set of actions executed by the first user.
 22. The system of claim 16,wherein the machine learning algorithms create outputs of attributesranking users against outcomes defined by the contact center and withinthe persona clusters.
 23. The system of claim 16, wherein the managementdecision comprises one of: identification of the first user as avaluable user within a persona set given an outcome to maximize, smartinteraction routing based on agent efficiency clusters, interaction typerecommendations to the selected second user to offer to the first userand offer timing, automatic interaction offers to the first user focusedon shaping customer journey, and automatic interaction offers to thefirst user focused on optimizing outcome.
 24. The method of claim 16,wherein the analyzing further comprises determining a ranked list forthe desired outcome, wherein the ranked list comprises which action tooffer the first user by a selected second user to complete the desiredoutcome.
 25. The method of claim 24, wherein the action comprises atleast one of: a voice call, a chat, email, and web page suggestions.